We present an approach for parameter estimation with multirate measurements, with the slow measurements having variable time delays due to laboratory analysis, and also being functions of all the states during the sample collection. We formulate a particle filter-based approach under the framework of the expectation maximization algorithm to develop the estimates. The effectiveness and applicability of the proposed method are demonstrated though a simulation example, a hybrid tank experiment and an industrial case study; in each case, the slow and fast measurements are for the same variable. We show that this approach results in improved parameter estimation when the information from the delayed measurements is fused with the fast measurement information.

中文翻译：

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具有多速率测量和随机延迟的非线性系统的参数估计

我们提出了一种使用多速率测量进行参数估计的方法，由于实验室分析，慢速测量具有可变的时间延迟，并且也是样本收集过程中所有状态的函数。我们在期望最大化算法的框架下制定了一种基于粒子滤波器的方法来开发估计。通过仿真实例、混合罐实验和工业案例研究证明了所提出方法的有效性和适用性；在每种情况下，慢速和快速测量都是针对相同的变量。我们表明，当来自延迟测量的信息与快速测量信息融合时，这种方法可以改进参数估计。